# clear environment
rm(list=ls())

# load packages
library(openxlsx)

# load data set with chapter lenghts and crime numbers
dat = openxlsx::read.xlsx("vsberichte_metadata.xlsx", sheet = 1)

# binary variable indicating whether interior minister is from a center-right party
dat$cr_intmin <- ifelse(dat$intmin_party == "cdu" | dat$intmin_party== "csu" | dat$intmin_party=="fdp", 1, ifelse(dat$intmin_party=="pro", NA, 0))

# remove those without center-right or center-left interior minister
dat <- dat[!is.na(dat$cr_intmin),]

# create chapter length variable
dat$rwe_chapter_length <- dat$page_right_end-dat$page_right_start
dat$lwe_chapter_length <- dat$page_left_end-dat$page_left_start

# ratio of chapter length
dat$rwe_lwe_chapter_ratio_log <- log(dat$rwe_chapter_length + 0.5) - log(dat$lwe_chapter_length + 0.5)

# remove those with NA in chapter ratio
dat <- dat[!is.na(dat$rwe_lwe_chapter_ratio_log),]

# ratio of violent crimes (killings and assaults)
dat$rwe_lwe_crime_violent_ratio_log <- log(dat$extreme_crime_violent_physical_right + 0.5) - log(dat$extreme_crime_violent_physical_left + 0.5)

# get crime ratio on federal level as a separate variable
crime_fed <- dat[dat$jurisdiction=="bund",c("year", "extreme_crime_violent_physical_right", "extreme_crime_violent_physical_left")]
names(crime_fed) <- c("year", "extreme_crime_violent_physical_right_fed", "extreme_crime_violent_physical_left_fed")
crime_fed$rwe_lwe_crime_violent_ratio_log_fed <- log(crime_fed$extreme_crime_violent_physical_right_fed + 0.5) - log(crime_fed$extreme_crime_violent_physical_left_fed + 0.5)
dat <- merge(dat, crime_fed, by="year", all.x=T)

# impute crime ratio with federal
dat$rwe_lwe_crime_violent_ratio_log_fedimp <- ifelse(is.na(dat$rwe_lwe_crime_violent_ratio_log),
                                                     dat$rwe_lwe_crime_violent_ratio_log_fed,
                                                     dat$rwe_lwe_crime_violent_ratio_log)

# get the difference in ratios of crime and chapter length
dat$bias_ratio_fedimp <- dat$rwe_lwe_chapter_ratio_log - dat$rwe_lwe_crime_violent_ratio_log_fedimp

# create decade indicator
dat$decade <- NA
dat$decade[dat$year<1960]<-1950
dat$decade[dat$year>=1960 & dat$year<1970]<-1960
dat$decade[dat$year>=1970 & dat$year<1980]<-1970
dat$decade[dat$year>=1980 & dat$year<1990]<-1980
dat$decade[dat$year>=1990 & dat$year<2000]<-1990
dat$decade[dat$year>=2000 & dat$year<2010]<-2000
dat$decade[dat$year>=2010 & dat$year<2020]<-2010
dat$decade[dat$year>=2020 & dat$year<2030]<-2020


## regressions models
# bias
mod_1 <- lm(bias_ratio_fedimp ~ factor(cr_intmin), data=dat[dat$decade==2000 | dat$decade==2010 | dat$decade==2020,])
mod_2 <- lm(bias_ratio_fedimp ~ factor(cr_intmin) + factor(jurisdiction), data=dat[dat$decade==2000 | dat$decade==2010 | dat$decade==2020,])
mod_3 <- lm(bias_ratio_fedimp ~ factor(cr_intmin) + factor(jurisdiction) + factor(decade), data=dat[dat$decade==2000 | dat$decade==2010 | dat$decade==2020,])
mod_4 <- lm(bias_ratio_fedimp ~ factor(cr_intmin) + factor(jurisdiction) + factor(year), data=dat[dat$decade==2000 | dat$decade==2010 | dat$decade==2020,])


